Questions on the Fundamentals for Various Sampling Methods

Hello, I've recently been studying various sampling methods, specifically SRS (Simple Random Sampling), Stratified Sampling, Ratio/Regression Estimation, and Cluster Sampling. I wanted to ask a few questions regarding these methods to better understand the pros and cons for each of them and when to use a specific method given particular situations.
Specifically, I wanted to ask, what are the benefits and weaknesses of each of these methods? When would it be appropriate to use each of these methods? How would we go about employing any of these methods onto a dataset? Are there any signs in a dataset or description of a study where one should immediately note which method is most useful for that particular case? Any help would be appreciated as I'd like to get a better understanding of all these sampling designs and such for the future.
As an extra question to slap on here, what exactly makes a good estimator for certain sampling methods? Would it be estimators that are unbiased?
Thank you in advance for any advice or guidance given.


Active Member
You can read about sampling methods here:

Robert, C. P., & Casella, G. (2004). Monte Carlo Statistical Methods (2nd ed.). New York: Springer.

The methods for developing efficient estimators under various circumstances are summarized here:

Lehmann, E. L., & Casella, G. (1998). Theory of Point Estimation (2nd ed). New York: Springer.